Data Management

The Wisconsin MRSEC’s Facilities and Data Management Group works to address the increasingly important integration of computational and data science methods with materials science research and education.

Efforts in Data Management include oversight of the Shared Computational Facilities and implementation of the Wisconsin MRSEC’s Data Management Plan. This includes coordinating the MRSEC Data Facility (for short-term storage and analysis of data from data-intense instruments) and a searchable MRSEC Products website (for long-term access and cataloging of curated data underlying all publications).

The group also works to develop best practices for data management and collaborates across the MRSEC. This has included developing a guidelines document, hosting the “Wisconsin MRSEC Excellence in Open Science Prize,” and many presentations at events. Professional development opportunities provided include instruction and mentoring in data management best practices.

Call for Submissions for the Wisconsin MRSEC Excellence in Open Science Prize

The MRSEC Excellence in Open Science Prize has been launched to promote data sharing and open science principles.

This prize recognizes a researcher or research team that has  demonstrated an exceptional effort and/or success in the development and/or dissemination of impactful data for the scientific community.

More info…

Data Facilities

The Data Facilities provide remote instrument access and data-analysis capabilities.

MSCData Cluster

MSCData is a hardware stack providing three homogeneous compute nodes, and a large, 144TB SAS-connected backend for the storage of Scanning Transmission Electron Microscopy (STEM) imaging data. Each compute node is a Dell PowerEdge R730 comprised of two Intel Xeon E5-2670v3 processors, 192GB RAM, and a NVIDIA Tesla M10 GPU (provides 4x logical GPUs to software). MSCData is curated by SBEL on behalf of the Voyles Group. It was assembled in January 2018.


MINDS@UW is designed to gather, distribute, and preserve digital materials related to the University of Wisconsin’s research and instructional mission. Content may include research papers and reports, pre-prints and post-prints, datasets and other primary research materials, learning objects, theses, student projects, conference papers and presentations, and other born-digital or digitized research and instructional materials. It is not intended to support the public accessibility and preservation of official institutional or campus records that would not normally be published in the course of university business.

Data Science Activities

The group has various Data Science activities.

Seed: High-Throughput Design of Responsive Liquid Crystal Materials & Scalable Machine Learning Algorithms

This project aims to develop general machine-learning methodologies to extract patterns from visual sources of information that arise in materials science (e.g., images, visual renderings of first-principle simulations, and networks). The techniques are applied to experimental datasets of visual renderings of high-throughput flow cytometry liquid crystal data and of molecular dynamic simulations. This work seeks to establish new strategies to map data to informative visual patterns that can be processed and interpreted using convolutional neural networks (CNNs).